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05/23/2023
On the Size and Approximation Error of Distilled Sets
Dataset Distillation is the task of synthesizing small datasets from lar...
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03/21/2023
Infrastructure-based End-to-End Learning and Prevention of Driver Failure
Intelligent intersection managers can improve safety by detecting danger...
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02/13/2023
Dataset Distillation with Convexified Implicit Gradients
We propose a new dataset distillation algorithm using reparameterization...
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02/02/2023
Dataset Distillation Fixes Dataset Reconstruction Attacks
Modern deep learning requires large volumes of data, which could contain...
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12/21/2022
Cooperative Flight Control Using Visual-Attention – Air-Guardian
The cooperation of a human pilot with an autonomous agent during flight ...
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11/29/2022
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
We study the problem of training and certifying adversarially robust qua...
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10/11/2022
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
We study the problem of learning controllers for discrete-time non-linea...
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10/11/2022
Learning Control Policies for Region Stabilization in Stochastic Systems
We consider the problem of learning control policies in stochastic syste...
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10/10/2022
PyHopper – Hyperparameter optimization
Hyperparameter tuning is a fundamental aspect of machine learning resear...
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10/09/2022
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
There is an ever-growing zoo of modern neural network models that can ef...
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09/26/2022
Liquid Structural State-Space Models
A proper parametrization of state transition matrices of linear state-sp...
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06/02/2022
Entangled Residual Mappings
Residual mappings have been shown to perform representation learning in ...
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05/24/2022
Learning Stabilizing Policies in Stochastic Control Systems
In this work, we address the problem of learning provably stable neural ...
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04/15/2022
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
Adversarial training (i.e., training on adversarially perturbed input da...
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12/17/2021
Stability Verification in Stochastic Control Systems via Neural Network Supermartingales
We consider the problem of formally verifying almost-sure (a.s.) asympto...
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11/04/2021
Infinite Time Horizon Safety of Bayesian Neural Networks
Bayesian neural networks (BNNs) place distributions over the weights of ...
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10/14/2021
Interactive Analysis of CNN Robustness
While convolutional neural networks (CNNs) have found wide adoption as s...
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07/18/2021
GoTube: Scalable Stochastic Verification of Continuous-Depth Models
We introduce a new stochastic verification algorithm that formally quant...
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06/25/2021
Closed-form Continuous-Depth Models
Continuous-depth neural models, where the derivative of the model's hidd...
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06/15/2021
Causal Navigation by Continuous-time Neural Networks
Imitation learning enables high-fidelity, vision-based learning of polic...
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06/13/2021
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Robustness to variations in lighting conditions is a key objective for a...
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03/15/2021
Adversarial Training is Not Ready for Robot Learning
Adversarial training is an effective method to train deep learning model...
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03/08/2021
Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars
Despite the rich theoretical foundation of model-based deep reinforcemen...
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12/16/2020
On The Verification of Neural ODEs with Stochastic Guarantees
We show that Neural ODEs, an emerging class of time-continuous neural ne...
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12/15/2020
Scalable Verification of Quantized Neural Networks (Technical Report)
Formal verification of neural networks is an active topic of research, a...
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12/14/2020
Lagrangian Reachtubes: The Next Generation
We introduce LRT-NG, a set of techniques and an associated toolset that ...
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06/08/2020
Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network mod...
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06/08/2020
Learning Long-Term Dependencies in Irregularly-Sampled Time Series
Recurrent neural networks (RNNs) with continuous-time hidden states are ...
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11/01/2018
Liquid Time-constant Recurrent Neural Networks as Universal Approximators
In this paper, we introduce the notion of liquid time-constant (LTC) rec...
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09/11/2018
Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks
We propose an effective method for creating interpretable control agents...
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09/11/2018
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
In this paper, we introduce a novel method to interpret recurrent neural...
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03/22/2018
Neuronal Circuit Policies
We propose an effective way to create interpretable control agents, by r...
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11/09/2017